Text Generation in Low-Resource Languages Using Human-Curated Vocabularies
Paper • 2610.37105 • Published
Datasets:
Tasks:
Text Generation
Size:
10K<n<100K
ArXiv:
Tags:
natural-language-processing
low-resource-languages
multilingual
text-generation
synthetic-data
data-generation
License:
Dataset Preview
The full dataset viewer is not available (click to read why). Only showing a preview of the rows.
The dataset generation failed because of a cast error
Error code: DatasetGenerationCastError
Exception: DatasetGenerationCastError
Message: An error occurred while generating the dataset
All the data files must have the same columns, but at some point there are 2 new columns ({'cultural_relevance_score', 'is_valid'}) and 3 missing columns ({'seed', 'sentence_match_rate', 'candidate_seed'}).
This happened while the csv dataset builder was generating data using
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'hf://datasets/SEACrowd-CoRaL/experiment_result@cad9c383af47cb2bae13e87855cae63ecd202ccc/outputs/topic_related/vietnamese_topic_related_gemini_thinking_on.csv', 'hf://datasets/SEACrowd-CoRaL/experiment_result@cad9c383af47cb2bae13e87855cae63ecd202ccc/outputs/topic_related/vietnamese_topic_related_openai_thinking_off.csv', 'hf://datasets/SEACrowd-CoRaL/experiment_result@cad9c383af47cb2bae13e87855cae63ecd202ccc/outputs/topic_related/vietnamese_topic_related_openai_thinking_on.csv', 'hf://datasets/SEACrowd-CoRaL/experiment_result@cad9c383af47cb2bae13e87855cae63ecd202ccc/prompts/few_shots_and_word_candidates/acehnese_few_shots_and_word_candidates.csv', 'hf://datasets/SEACrowd-CoRaL/experiment_result@cad9c383af47cb2bae13e87855cae63ecd202ccc/prompts/few_shots_and_word_candidates/hitu_few_shots_and_word_candidates.csv', 'hf://datasets/SEACrowd-CoRaL/experiment_result@cad9c383af47cb2bae13e87855cae63ecd202ccc/prompts/few_shots_and_word_candidates/indonesian_few_shots_and_word_candidates.csv', 'hf://datasets/SEACrowd-CoRaL/experiment_result@cad9c383af47cb2bae13e87855cae63ecd202ccc/prompts/few_shots_and_word_candidates/javanese_few_shots_and_word_candidates.csv', 'hf://datasets/SEACrowd-CoRaL/experiment_result@cad9c383af47cb2bae13e87855cae63ecd202ccc/prompts/few_shots_and_word_candidates/minangkabau_few_shots_and_word_candidates.csv', 'hf://datasets/SEACrowd-CoRaL/experiment_result@cad9c383af47cb2bae13e87855cae63ecd202ccc/prompts/few_shots_and_word_candidates/vietnamese_few_shots_and_word_candidates.csv', 'hf://datasets/SEACrowd-CoRaL/experiment_result@cad9c383af47cb2bae13e87855cae63ecd202ccc/prompts/few_shots_only/acehnese_few_shots_only.csv', 'hf://datasets/SEACrowd-CoRaL/experiment_result@cad9c383af47cb2bae13e87855cae63ecd202ccc/prompts/few_shots_only/hitu_few_shots_only.csv', 'hf://datasets/SEACrowd-CoRaL/experiment_result@cad9c383af47cb2bae13e87855cae63ecd202ccc/prompts/few_shots_only/indonesian_few_shots_only.csv', 'hf://datasets/SEACrowd-CoRaL/experiment_result@cad9c383af47cb2bae13e87855cae63ecd202ccc/prompts/few_shots_only/javanese_few_shots_only.csv', 'hf://datasets/SEACrowd-CoRaL/experiment_result@cad9c383af47cb2bae13e87855cae63ecd202ccc/prompts/few_shots_only/minangkabau_few_shots_only.csv', 'hf://datasets/SEACrowd-CoRaL/experiment_result@cad9c383af47cb2bae13e87855cae63ecd202ccc/prompts/few_shots_only/vietnamese_few_shots_only.csv', 'hf://datasets/SEACrowd-CoRaL/experiment_result@cad9c383af47cb2bae13e87855cae63ecd202ccc/prompts/topic_related/acehnese_topic_related.csv', 'hf://datasets/SEACrowd-CoRaL/experiment_result@cad9c383af47cb2bae13e87855cae63ecd202ccc/prompts/topic_related/hitu_topic_related.csv', 'hf://datasets/SEACrowd-CoRaL/experiment_result@cad9c383af47cb2bae13e87855cae63ecd202ccc/prompts/topic_related/indonesian_topic_related.csv', 'hf://datasets/SEACrowd-CoRaL/experiment_result@cad9c383af47cb2bae13e87855cae63ecd202ccc/prompts/topic_related/javanese_topic_related.csv', 'hf://datasets/SEACrowd-CoRaL/experiment_result@cad9c383af47cb2bae13e87855cae63ecd202ccc/prompts/topic_related/minangkabau_topic_related.csv', 'hf://datasets/SEACrowd-CoRaL/experiment_result@cad9c383af47cb2bae13e87855cae63ecd202ccc/prompts/topic_related/vietnamese_topic_related.csv']
Please either edit the data files to have matching columns, or separate them into different configurations (see docs at https://hf.co/docs/hub/datasets-manual-configuration#multiple-configurations)
Traceback: Traceback (most recent call last):
File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1856, in _prepare_split_single
writer.write_table(table)
~~~~~~~~~~~~~~~~~~^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/arrow_writer.py", line 811, in write_table
self._write_table(pa_table, writer_batch_size=writer_batch_size)
~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/arrow_writer.py", line 822, in _write_table
pa_table = table_cast(pa_table, self._schema)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2381, in table_cast
return cast_table_to_schema(table, schema)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2309, in cast_table_to_schema
raise CastError(
...<3 lines>...
)
datasets.table.CastError: Couldn't cast
prompt_id: string
language: string
length_category: string
unit: string
target_length_desc: string
prompt_type: string
topic: double
prompt: double
output_id: string
output: string
input_words: int64
output_words: int64
input_tokens: int64
output_tokens: int64
total_tokens: int64
processing_time_sec: double
full_response: string
model: string
is_thinking_on: bool
is_valid: bool
fluency_score: int64
cultural_relevance_score: int64
pct_loan_words: double
-- schema metadata --
pandas: '{"index_columns": [{"kind": "range", "name": null, "start": 0, "' + 3074
to
{'prompt_id': Value('string'), 'language': Value('string'), 'topic': Value('string'), 'prompt': Value('float64'), 'seed': Value('int64'), 'candidate_seed': Value('int64'), 'length_category': Value('string'), 'unit': Value('string'), 'target_length_desc': Value('string'), 'prompt_type': Value('string'), 'output_id': Value('string'), 'output': Value('string'), 'input_words': Value('int64'), 'output_words': Value('int64'), 'input_tokens': Value('int64'), 'output_tokens': Value('int64'), 'total_tokens': Value('int64'), 'processing_time_sec': Value('float64'), 'full_response': Value('string'), 'model': Value('string'), 'is_thinking_on': Value('bool'), 'fluency_score': Value('int64'), 'sentence_match_rate': Value('float64'), 'pct_loan_words': Value('float64')}
because column names don't match
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1379, in compute_config_parquet_and_info_response
parquet_operations, partial, estimated_dataset_info = stream_convert_to_parquet(
~~~~~~~~~~~~~~~~~~~~~~~~~^
builder, max_dataset_size_bytes=max_dataset_size_bytes
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
)
^
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 956, in stream_convert_to_parquet
builder._prepare_split(split_generator=splits_generators[split], file_format="parquet")
~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1702, in _prepare_split
for job_id, done, content in self._prepare_split_single(
~~~~~~~~~~~~~~~~~~~~~~~~~~^
gen_kwargs=gen_kwargs, job_id=job_id, **_prepare_split_args
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
):
^
File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1858, in _prepare_split_single
raise DatasetGenerationCastError.from_cast_error(
...<4 lines>...
)
datasets.exceptions.DatasetGenerationCastError: An error occurred while generating the dataset
All the data files must have the same columns, but at some point there are 2 new columns ({'cultural_relevance_score', 'is_valid'}) and 3 missing columns ({'seed', 'sentence_match_rate', 'candidate_seed'}).
This happened while the csv dataset builder was generating data using
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'hf://datasets/SEACrowd-CoRaL/experiment_result@cad9c383af47cb2bae13e87855cae63ecd202ccc/outputs/few_shots_and_word_candidates/acehnese_few_shots_and_word_candidates_openai_thinking_off.csv', 'hf://datasets/SEACrowd-CoRaL/experiment_result@cad9c383af47cb2bae13e87855cae63ecd202ccc/outputs/few_shots_and_word_candidates/acehnese_few_shots_and_word_candidates_openai_thinking_on.csv', 'hf://datasets/SEACrowd-CoRaL/experiment_result@cad9c383af47cb2bae13e87855cae63ecd202ccc/outputs/few_shots_and_word_candidates/hitu_few_shots_and_word_candidates_claude_thinking_off.csv', 'hf://datasets/SEACrowd-CoRaL/experiment_result@cad9c383af47cb2bae13e87855cae63ecd202ccc/outputs/few_shots_and_word_candidates/hitu_few_shots_and_word_candidates_claude_thinking_on.csv', 'hf://datasets/SEACrowd-CoRaL/experiment_result@cad9c383af47cb2bae13e87855cae63ecd202ccc/outputs/few_shots_and_word_candidates/hitu_few_shots_and_word_candidates_gemini_thinking_off.csv', 'hf://datasets/SEACrowd-CoRaL/experiment_result@cad9c383af47cb2bae13e87855cae63ecd202ccc/outputs/few_shots_and_word_candidates/hitu_few_shots_and_word_candidates_gemini_thinking_on.csv', 'hf://datasets/SEACrowd-CoRaL/experiment_result@cad9c383af47cb2bae13e87855cae63ecd202ccc/outputs/few_shots_and_word_candidates/hitu_few_shots_and_word_candidates_openai_thinking_off.csv', 'hf://datasets/SEACrowd-CoRaL/experiment_result@cad9c383af47cb2bae13e87855cae63ecd202ccc/outputs/few_shots_and_word_candidates/hitu_few_shots_and_word_candidates_openai_thinking_on.csv', 'hf://datasets/SEACrowd-CoRaL/experiment_result@cad9c383af47cb2bae13e87855cae63ecd202ccc/outputs/few_shots_and_word_candidates/indonesian_few_shots_and_word_candidates_claude_thinking_off.csv', 'hf://datasets/SEACrowd-CoRaL/experiment_result@cad9c383af47cb2bae13e87855cae63ecd202ccc/outputs/few_shots_and_word_candidates/indonesian_few_shots_and_word_candidates_claude_thinking_on.csv', 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'hf://datasets/SEACrowd-CoRaL/experiment_result@cad9c383af47cb2bae13e87855cae63ecd202ccc/outputs/few_shots_and_word_candidates/javanese_few_shots_and_word_candidates_claude_thinking_on.csv', 'hf://datasets/SEACrowd-CoRaL/experiment_result@cad9c383af47cb2bae13e87855cae63ecd202ccc/outputs/few_shots_and_word_candidates/javanese_few_shots_and_word_candidates_gemini_thinking_off.csv', 'hf://datasets/SEACrowd-CoRaL/experiment_result@cad9c383af47cb2bae13e87855cae63ecd202ccc/outputs/few_shots_and_word_candidates/javanese_few_shots_and_word_candidates_gemini_thinking_on.csv', 'hf://datasets/SEACrowd-CoRaL/experiment_result@cad9c383af47cb2bae13e87855cae63ecd202ccc/outputs/few_shots_and_word_candidates/javanese_few_shots_and_word_candidates_openai_thinking_off.csv', 'hf://datasets/SEACrowd-CoRaL/experiment_result@cad9c383af47cb2bae13e87855cae63ecd202ccc/outputs/few_shots_and_word_candidates/javanese_few_shots_and_word_candidates_openai_thinking_on.csv', 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Please either edit the data files to have matching columns, or separate them into different configurations (see docs at https://hf.co/docs/hub/datasets-manual-configuration#multiple-configurations)Need help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
prompt_id string | language string | topic string | prompt null | seed int64 | candidate_seed int64 | length_category string | unit string | target_length_desc string | prompt_type string | output_id string | output string | input_words int64 | output_words int64 | input_tokens int64 | output_tokens int64 | total_tokens int64 | processing_time_sec float64 | full_response string | model string | is_thinking_on bool | fluency_score int64 | sentence_match_rate float64 | pct_loan_words float64 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
33818197-cad3-4e95-b429-31593fb43f8e | acehnese | art | null | 42 | 1,624,623,205 | 5_words | sentence | 5 words | few_shots_and_word_candidates | 7b0626ad-ee6d-4be9-8f53-8dd57697feab | Aneuk nanggroe kuta meulagu lantang. | 12,307 | 5 | 21,532 | 18 | 21,550 | 1.16 | {"id": "msg_011CfJVY5HHir4mK6KVdvDMJ", "container": null, "content": [{"citations": null, "text": "Aneuk nanggroe kuta meulagu lantang.", "type": "text"}], "model": "claude-haiku-4-5-20251001", "role": "assistant", "stop_details": null, "stop_reason": "end_turn", "stop_sequence": null, "type": "message", "usage": {"cac... | claude-haiku-4-5 | false | 1 | 1 | 0 |
0c90c1fe-a9ec-40f1-8db4-b737d7300ccc | acehnese | pets | null | 42 | 711,425,035 | 5_words | sentence | 5 words | few_shots_and_word_candidates | 752c8713-f13e-42b5-bb83-d115cfabe997 | Uroe nyoe abu keumawè lam krueng. | 12,240 | 6 | 21,307 | 18 | 21,325 | 1.18 | {"id": "msg_011CfJVY5FoX1uyQB2L5V2g7", "container": null, "content": [{"citations": null, "text": "Uroe nyoe abu keumawè lam krueng.", "type": "text"}], "model": "claude-haiku-4-5-20251001", "role": "assistant", "stop_details": null, "stop_reason": "end_turn", "stop_sequence": null, "type": "message", "usage": {"cache_... | claude-haiku-4-5 | false | 5 | 0.8333 | 0 |
76f961ca-65f1-4b6e-9abb-e108db3a7da6 | acehnese | food | null | 42 | 296,110,802 | 5_words | sentence | 5 words | few_shots_and_word_candidates | 15434f62-cf8b-41cb-9d8b-c02d286f4d99 | Ayah bri bantuan keu gurèe nyan. | 12,242 | 6 | 21,418 | 18 | 21,436 | 1.19 | {"id": "msg_011CfJVY5TCzGy8VfULTrDV7", "container": null, "content": [{"citations": null, "text": "Ayah bri bantuan keu gurèe nyan.", "type": "text"}], "model": "claude-haiku-4-5-20251001", "role": "assistant", "stop_details": null, "stop_reason": "end_turn", "stop_sequence": null, "type": "message", "usage": {"cache_c... | claude-haiku-4-5 | false | 5 | 1 | 0 |
531eedc6-ce67-481d-ab1d-5e4f5eacc07b | acehnese | gaming | null | 42 | 1,470,657,397 | 5_words | sentence | 5 words | few_shots_and_word_candidates | 731de7f1-37fc-4d31-bfe3-a109c85703d7 | Buku nyan wareuna trang. | 12,216 | 4 | 21,191 | 13 | 21,204 | 1.19 | {"id": "msg_011CfJVY5RU3HCUNqiuj3Sdx", "container": null, "content": [{"citations": null, "text": "Buku nyan wareuna trang.", "type": "text"}], "model": "claude-haiku-4-5-20251001", "role": "assistant", "stop_details": null, "stop_reason": "end_turn", "stop_sequence": null, "type": "message", "usage": {"cache_creation"... | claude-haiku-4-5 | false | 5 | 1 | 0 |
db1c2c66-bf70-41a6-8a44-3101200dae7d | acehnese | travel | null | 42 | 1,609,270,712 | 5_words | sentence | 5 words | few_shots_and_word_candidates | 2612c5bc-f8c3-4289-be11-0910533ad846 | Ayah meusigo u peukan. | 12,259 | 4 | 21,204 | 13 | 21,217 | 1.18 | {"id": "msg_011CfJVY5YuRRXdGUV6atNsg", "container": null, "content": [{"citations": null, "text": "Ayah meusigo u peukan.", "type": "text"}], "model": "claude-haiku-4-5-20251001", "role": "assistant", "stop_details": null, "stop_reason": "end_turn", "stop_sequence": null, "type": "message", "usage": {"cache_creation": ... | claude-haiku-4-5 | false | 5 | 0.75 | 0 |
7c77dddf-b7ec-4c81-b176-d64123bcd91f | acehnese | garden | null | 42 | 82,279,157 | 5_words | sentence | 5 words | few_shots_and_word_candidates | 6214e310-6f26-4c1f-82ff-28ee889c0adb | Buku bak pustaka na dhiet that. | 12,164 | 6 | 20,843 | 15 | 20,858 | 1.23 | {"id": "msg_011CfJVY596m754KUe1sqCVu", "container": null, "content": [{"citations": null, "text": "Buku bak pustaka na dhiet that.", "type": "text"}], "model": "claude-haiku-4-5-20251001", "role": "assistant", "stop_details": null, "stop_reason": "end_turn", "stop_sequence": null, "type": "message", "usage": {"cache_cr... | claude-haiku-4-5 | false | 5 | 1 | 0 |
bc830b8e-bdd2-48d6-a356-03c2ec1ade11 | acehnese | nature | null | 42 | 1,518,037,085 | 5_words | sentence | 5 words | few_shots_and_word_candidates | 4342c673-6060-4a08-a0cd-96690a4d8f34 | Kamoe meurunoe buku bak blang. | 12,148 | 5 | 21,086 | 17 | 21,103 | 1.22 | {"id": "msg_011CfJVY5VCAuT1iJtDo2vo1", "container": null, "content": [{"citations": null, "text": "Kamoe meurunoe buku bak blang.", "type": "text"}], "model": "claude-haiku-4-5-20251001", "role": "assistant", "stop_details": null, "stop_reason": "end_turn", "stop_sequence": null, "type": "message", "usage": {"cache_cre... | claude-haiku-4-5 | false | 5 | 0.8 | 0 |
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66115068-c2c8-4af3-b0ec-4356240b50b5 | acehnese | home | null | 42 | 654,870,107 | 5_words | sentence | 5 words | few_shots_and_word_candidates | 28347e1e-f4a6-40c6-87af-97f322b7d574 | Gata hawa neuk udép bahgia. | 12,193 | 5 | 21,207 | 15 | 21,222 | 1.01 | {"id": "msg_011CfJVYPcMi5rV41Wa6PhMc", "container": null, "content": [{"citations": null, "text": "Gata hawa neuk udép bahgia.", "type": "text"}], "model": "claude-haiku-4-5-20251001", "role": "assistant", "stop_details": null, "stop_reason": "end_turn", "stop_sequence": null, "type": "message", "usage": {"cache_creati... | claude-haiku-4-5 | false | 5 | 0.6 | 0 |
82356d02-2a60-476a-9e6e-bd3873d4a2af | acehnese | health | null | 42 | 1,902,852,732 | 5_words | sentence | 5 words | few_shots_and_word_candidates | b4729745-8fa1-4f6d-8e9f-bae6098cf494 | Iman makmu that ngen seulamat. | 12,628 | 5 | 22,347 | 16 | 22,363 | 1.02 | {"id": "msg_011CfJVYQ8MbZCMjugjtwaiz", "container": null, "content": [{"citations": null, "text": "Iman makmu that ngen seulamat.", "type": "text"}], "model": "claude-haiku-4-5-20251001", "role": "assistant", "stop_details": null, "stop_reason": "end_turn", "stop_sequence": null, "type": "message", "usage": {"cache_cre... | claude-haiku-4-5 | false | 5 | 1 | 0 |
767ca111-34c6-4087-9173-1013cd844336 | acehnese | movies | null | 42 | 1,638,012,556 | 5_words | sentence | 5 words | few_shots_and_word_candidates | a41ad11b-cd9e-41aa-8669-ae9e1b4364fd | Krueng raya nyan teungoh ilé u laôt. | 12,134 | 7 | 21,097 | 20 | 21,117 | 1.45 | {"id": "msg_011CfJVYNZMZgV5eAvykZva6", "container": null, "content": [{"citations": null, "text": "Krueng raya nyan teungoh ilé u laôt.", "type": "text"}], "model": "claude-haiku-4-5-20251001", "role": "assistant", "stop_details": null, "stop_reason": "end_turn", "stop_sequence": null, "type": "message", "usage": {"cac... | claude-haiku-4-5 | false | 5 | 0.4286 | 0 |
3c3f3565-490c-47bb-a49f-d715ee0993c5 | acehnese | technology | null | 42 | 1,931,930,977 | 5_words | sentence | 5 words | few_shots_and_word_candidates | 87ae7e5e-d152-4771-9e00-e261e4bc0199 | Mak teumuleh buku bak blang. | 12,353 | 5 | 21,637 | 16 | 21,653 | 1.24 | {"id": "msg_011CfJVYQGJUduL2TmSvUCTu", "container": null, "content": [{"citations": null, "text": "Mak teumuleh buku bak blang.", "type": "text"}], "model": "claude-haiku-4-5-20251001", "role": "assistant", "stop_details": null, "stop_reason": "end_turn", "stop_sequence": null, "type": "message", "usage": {"cache_creat... | claude-haiku-4-5 | false | 5 | 1 | 0 |
0d6fed02-21b4-435b-8693-c6dd95a11734 | acehnese | home | null | 42 | 640,256,953 | 5_words | sentence | 5 words | few_shots_and_word_candidates | 841ef40d-0ec1-4bd3-8d15-2b71aff932c7 | Asèe nyan jiéh bak awai uroe. | 12,449 | 6 | 21,692 | 19 | 21,711 | 1.11 | {"id": "msg_011CfJVYQYv6cAhVhBAF3cs9", "container": null, "content": [{"citations": null, "text": "Asèe nyan jiéh bak awai uroe.", "type": "text"}], "model": "claude-haiku-4-5-20251001", "role": "assistant", "stop_details": null, "stop_reason": "end_turn", "stop_sequence": null, "type": "message", "usage": {"cache_crea... | claude-haiku-4-5 | false | 5 | 0.6667 | 0 |
38a39286-5e0d-4acb-a07a-ce8634c21e54 | acehnese | nature | null | 42 | 1,666,285,562 | 5_words | sentence | 5 words | few_shots_and_word_candidates | 8ec560f5-2dc5-4897-b868-6d8c5be5aab9 | Ineng mak teungoh meurunoe. | 12,189 | 4 | 21,200 | 16 | 21,216 | 1.04 | {"id": "msg_011CfJVYQwyQMCL8PyfdkE76", "container": null, "content": [{"citations": null, "text": "Ineng mak teungoh meurunoe.", "type": "text"}], "model": "claude-haiku-4-5-20251001", "role": "assistant", "stop_details": null, "stop_reason": "end_turn", "stop_sequence": null, "type": "message", "usage": {"cache_creati... | claude-haiku-4-5 | false | 3 | 0.5 | 0 |
af5d874e-1fce-4a14-9c14-841c8fe10c97 | acehnese | books | null | 42 | 1,765,893,696 | 5_words | sentence | 5 words | few_shots_and_word_candidates | e14096bb-d266-46e4-b052-f35fecc329fe | Ayah mè ubat keu blang. | 12,229 | 5 | 21,283 | 15 | 21,298 | 1.05 | {"id": "msg_011CfJVYRHJyht4jyy4u1mhu", "container": null, "content": [{"citations": null, "text": "Ayah mè ubat keu blang.", "type": "text"}], "model": "claude-haiku-4-5-20251001", "role": "assistant", "stop_details": null, "stop_reason": "end_turn", "stop_sequence": null, "type": "message", "usage": {"cache_creation":... | claude-haiku-4-5 | false | 5 | 1 | 0 |
9e93d93c-7737-48c4-b0e6-51079297128a | acehnese | travel | null | 42 | 1,303,020,419 | 5_words | sentence | 5 words | few_shots_and_word_candidates | cdf41fa4-3a84-4c15-b631-fb06d49c38db | Aneuk miet teumuleh ngen buku. | 12,173 | 5 | 20,883 | 17 | 20,900 | 1.97 | {"id": "msg_011CfJVYMT8aCTDxAb5GVgED", "container": null, "content": [{"citations": null, "text": "Aneuk miet teumuleh ngen buku.", "type": "text"}], "model": "claude-haiku-4-5-20251001", "role": "assistant", "stop_details": null, "stop_reason": "end_turn", "stop_sequence": null, "type": "message", "usage": {"cache_cre... | claude-haiku-4-5 | false | 5 | 0.6 | 0 |
6317d9ba-e954-4aea-a3ad-9afc0dff564e | acehnese | fitness | null | 42 | 2,140,402,046 | 5_words | sentence | 5 words | few_shots_and_word_candidates | 4b0975e8-1e1c-4e7b-9f52-50d0353596d0 | Manok crôh ngen ie kupi mangat. | 12,323 | 6 | 21,733 | 17 | 21,750 | 1.44 | {"id": "msg_011CfJVYREpsJ8yHk9EWZxvY", "container": null, "content": [{"citations": null, "text": "Manok crôh ngen ie kupi mangat.", "type": "text"}], "model": "claude-haiku-4-5-20251001", "role": "assistant", "stop_details": null, "stop_reason": "end_turn", "stop_sequence": null, "type": "message", "usage": {"cache_cr... | claude-haiku-4-5 | false | 5 | 0.8333 | 0 |
End of preview.
Synthetic Text Generation in Low-Resource Southeast Asian Languages
Overview
This dataset contains 54,000 synthetically generated text samples across six Southeast Asian languages, produced through a systematic evaluation of large language models (LLMs) for low-resource language text generation.
The dataset accompanies the research paper:
Text Generation in Low-Resource Languages Using Human-Curated Vocabularies
The research investigates whether LLMs can generate coherent text in low-resource and extremely low-resource languages by leveraging human-curated dictionaries, word candidates, and human-written exemplars.
Our approach combines human-curated vocabulary resources with few-shot prompting and Zipfian word sampling to improve lexical coverage and text generation quality.
Research Paper
- Paper: Text Generation in Low-Resource Languages Using Human-Curated Vocabularies
- Implementation: vocab2text (GitHub)
Please refer to the paper for the complete methodology, experimental analysis, and evaluation results.
Dataset Composition
The dataset is generated from an experimental setup consisting of five dimensions:
| Dimension | Configurations |
|---|---|
| Languages | 6 |
| Language Models | 3 |
| Thinking Modes | 2 |
| Prompting Strategies | 3 |
| Text Lengths | 5 |
| Total Configurations | 540 |
| Outputs per Configuration | 100 |
| Total Generated Samples | 54,000 |
Languages
We experiment with six Southeast Asian languages spanning different levels of resource availability.
| Language | ISO 639-3 Code | Resource Availability |
|---|---|---|
| Indonesian | ind |
Medium-resource |
| Vietnamese | vie |
Medium-resource |
| Javanese | jav |
Low-resource |
| Minangkabau | min |
Low-resource |
| Acehnese | ace |
Extremely low-resource |
| Hitu | htu |
Extremely low-resource |
Language Models and Thinking Modes
Three LLMs are evaluated under two reasoning configurations:
| Model | Thinking Enabled | Thinking Disabled |
|---|---|---|
| Gemini 3.5 Flash | ✓ | ✓ |
| Claude Haiku 4.5 | ✓ | ✓ |
| GPT-5.4 Mini | ✓ | ✓ |
This results in six model configurations.
Prompting Strategies
We evaluate three prompting strategies that differ in the amount of linguistic information provided to the LLM.
Topic-Conditioned Zero-Shot Prompt (Zero-Shot)
Instructs the model to generate text in the target language about a specified topic without providing additional linguistic examples or vocabulary candidates.
Few-Shot Exemplars Only Prompt (Few-Shot)
Provides human-written exemplars of the target language and requested text length to guide generation.
Few-Shot Exemplars and Word Candidates Prompt (Ours)
Combines human-written exemplars with candidate words sampled from human-curated dictionaries using a Zipfian distribution. This is the proposed vocabulary-guided generation strategy.
Text Lengths
Five target text lengths are considered:
| Category | Target Length |
|---|---|
| Short sentence | 5 words |
| Sentence | 10 words |
| Sentence | 15 words |
| Long sentence | 20 words |
| Paragraph | 3 sentences and 25 words |
Citation
If you use this dataset in your research, please cite the accompanying paper:
@misc{sinanta2026textgenerationlowresourcelanguages,
title={Text Generation in Low-Resource Languages Using Human-Curated Vocabularies},
author={Michael Christlambert Sinanta and Muhammad Dehan Al Kautsar and Amanda Nurul Izzah and Thanh-Nhi Nguyen and Phan Phuong Mai Chau and Zheng Wei Lim and Fajri Koto},
year={2026},
eprint={2610.37105},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2610.37105},
}
License
This dataset is released under the Apache License 2.0.
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